Overview

Dataset statistics

Number of variables20
Number of observations587554
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory66.1 MiB
Average record size in memory118.0 B

Variable types

Numeric16
DateTime1
Categorical3

Alerts

AdGroupName has a high cardinality: 1557 distinct valuesHigh cardinality
Criteria has a high cardinality: 4262 distinct valuesHigh cardinality
AdGroupId is highly overall correlated with CampaignId and 1 other fieldsHigh correlation
CampaignId is highly overall correlated with AdGroupId and 1 other fieldsHigh correlation
AbsoluteTopImpressionPercentage is highly overall correlated with TopImpressionPercentage and 7 other fieldsHigh correlation
TopImpressionPercentage is highly overall correlated with AbsoluteTopImpressionPercentage and 3 other fieldsHigh correlation
SearchImpressionShare is highly overall correlated with AbsoluteTopImpressionPercentage and 7 other fieldsHigh correlation
SearchTopImpressionShare is highly overall correlated with AbsoluteTopImpressionPercentage and 7 other fieldsHigh correlation
Impressions is highly overall correlated with AbsoluteTopImpressionPercentage and 7 other fieldsHigh correlation
Clicks is highly overall correlated with AbsoluteTopImpressionPercentage and 6 other fieldsHigh correlation
Cost is highly overall correlated with AbsoluteTopImpressionPercentage and 6 other fieldsHigh correlation
Sessions is highly overall correlated with AbsoluteTopImpressionPercentage and 6 other fieldsHigh correlation
Cost_gbp is highly overall correlated with AbsoluteTopImpressionPercentage and 6 other fieldsHigh correlation
Margin is highly overall correlated with ROI_gbpHigh correlation
ROI_gbp is highly overall correlated with MarginHigh correlation
CampaignName is highly overall correlated with AdGroupId and 1 other fieldsHigh correlation
Impressions is highly skewed (γ1 = 29.86271315)Skewed
Clicks is highly skewed (γ1 = 31.81868362)Skewed
Cost is highly skewed (γ1 = 34.58918393)Skewed
Sessions is highly skewed (γ1 = 31.81933403)Skewed
Cost_gbp is highly skewed (γ1 = 34.58970642)Skewed
Margin is highly skewed (γ1 = 32.55780029)Skewed
ROI_gbp is highly skewed (γ1 = 68.26132965)Skewed
AbsoluteTopImpressionPercentage has 314320 (53.5%) zerosZeros
TopImpressionPercentage has 236197 (40.2%) zerosZeros
SearchImpressionShare has 193541 (32.9%) zerosZeros
SearchTopImpressionShare has 193576 (32.9%) zerosZeros
SearchRankLostTopImpressionShare has 282797 (48.1%) zerosZeros
Impressions has 228327 (38.9%) zerosZeros
Clicks has 370319 (63.0%) zerosZeros
Cost has 370319 (63.0%) zerosZeros
Sessions has 370324 (63.0%) zerosZeros
Cost_gbp has 370324 (63.0%) zerosZeros
Margin has 568930 (96.8%) zerosZeros
ROI_gbp has 571589 (97.3%) zerosZeros

Reproduction

Analysis started2023-03-09 09:22:37.603575
Analysis finished2023-03-09 09:24:10.827635
Duration1 minute and 33.22 seconds
Software versionydata-profiling vv4.1.0
Download configurationconfig.json

Variables

AdGroupId
Real number (ℝ)

Distinct1557
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4350075 × 1011
Minimum1.3837406 × 1011
Maximum1.4905426 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 MiB
2023-03-09T09:24:10.989773image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.3837406 × 1011
5-th percentile1.4074527 × 1011
Q11.4228448 × 1011
median1.4366583 × 1011
Q31.4386994 × 1011
95-th percentile1.4637224 × 1011
Maximum1.4905426 × 1011
Range1.0680203 × 1010
Interquartile range (IQR)1.5854578 × 109

Descriptive statistics

Standard deviation1.9838267 × 109
Coefficient of variation (CV)0.013824505
Kurtosis-0.66848392
Mean1.4350075 × 1011
Median Absolute Deviation (MAD)1.3813464 × 109
Skewness0.35668222
Sum8.4314437 × 1016
Variance3.9355684 × 1018
MonotonicityNot monotonic
2023-03-09T09:24:11.176897image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.463722288 × 10118083
 
1.4%
1.436658239 × 10115754
 
1.0%
1.463722283 × 10115617
 
1.0%
1.436658277 × 10113973
 
0.7%
1.463722298 × 10113699
 
0.6%
1.436658288 × 10113699
 
0.6%
1.46372229 × 10113699
 
0.6%
1.463722279 × 10113425
 
0.6%
1.463722278 × 10113288
 
0.6%
1.407452699 × 10113077
 
0.5%
Other values (1547) 543240
92.5%
ValueCountFrequency (%)
1.383740608 × 101174
 
< 0.1%
1.383740634 × 101174
 
< 0.1%
1.383740701 × 101174
 
< 0.1%
1.383740761 × 101174
 
< 0.1%
1.391787481 × 101174
 
< 0.1%
1.407452488 × 1011137
< 0.1%
1.407452489 × 1011274
< 0.1%
1.407452491 × 1011274
< 0.1%
1.407452491 × 1011137
< 0.1%
1.407452495 × 1011137
< 0.1%
ValueCountFrequency (%)
1.490542636 × 1011274
< 0.1%
1.490542634 × 1011137
< 0.1%
1.490542634 × 1011137
< 0.1%
1.490542633 × 1011137
< 0.1%
1.490542632 × 1011274
< 0.1%
1.490542631 × 1011137
< 0.1%
1.490542631 × 1011274
< 0.1%
1.490542629 × 1011137
< 0.1%
1.490542629 × 1011137
< 0.1%
1.490542629 × 1011137
< 0.1%

CampaignId
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9031107 × 1010
Minimum1.9026017 × 1010
Maximum1.9033111 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 MiB
2023-03-09T09:24:11.299854image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.9026017 × 1010
5-th percentile1.9026017 × 1010
Q11.903261 × 1010
median1.903261 × 1010
Q31.9032615 × 1010
95-th percentile1.9032616 × 1010
Maximum1.9033111 × 1010
Range7094642
Interquartile range (IQR)5073

Descriptive statistics

Standard deviation2774791
Coefficient of variation (CV)0.00014580292
Kurtosis-0.33683575
Mean1.9031107 × 1010
Median Absolute Deviation (MAD)5073
Skewness-1.2888598
Sum1.1181803 × 1016
Variance7.6994653 × 1012
MonotonicityNot monotonic
2023-03-09T09:24:11.420669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1.903261505 × 1010237889
40.5%
1.903260998 × 1010174199
29.6%
1.902601654 × 1010134540
22.9%
1.903261554 × 101032928
 
5.6%
1.903311119 × 10105669
 
1.0%
1.903261861 × 10102329
 
0.4%
ValueCountFrequency (%)
1.902601654 × 1010134540
22.9%
1.903260998 × 1010174199
29.6%
1.903261505 × 1010237889
40.5%
1.903261554 × 101032928
 
5.6%
1.903261861 × 10102329
 
0.4%
1.903311119 × 10105669
 
1.0%
ValueCountFrequency (%)
1.903311119 × 10105669
 
1.0%
1.903261861 × 10102329
 
0.4%
1.903261554 × 101032928
 
5.6%
1.903261505 × 1010237889
40.5%
1.903260998 × 1010174199
29.6%
1.902601654 × 1010134540
22.9%

CriterionId
Real number (ℝ)

Distinct4320
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5531408 × 1011
Minimum10341671
Maximum1.906812 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 MiB
2023-03-09T09:24:11.594842image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum10341671
5-th percentile4.7335553 × 108
Q11.7859937 × 1010
median3.2558629 × 1011
Q31.1622141 × 1012
95-th percentile1.7134322 × 1012
Maximum1.906812 × 1012
Range1.9068017 × 1012
Interquartile range (IQR)1.1443541 × 1012

Descriptive statistics

Standard deviation5.5436262 × 1011
Coefficient of variation (CV)0.99828664
Kurtosis-0.8938073
Mean5.5531408 × 1011
Median Absolute Deviation (MAD)3.2170802 × 1011
Skewness0.72089943
Sum3.2627701 × 1017
Variance3.0731792 × 1023
MonotonicityNot monotonic
2023-03-09T09:24:12.044936image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.380522169 × 1011274
 
< 0.1%
1.715518424 × 1012211
 
< 0.1%
1.712847985 × 1012211
 
< 0.1%
1.71343222 × 1012211
 
< 0.1%
1.713432216 × 1012211
 
< 0.1%
1.715180489 × 1012211
 
< 0.1%
1.743196683 × 1012211
 
< 0.1%
1.7131332 × 1012211
 
< 0.1%
1.713432224 × 1012211
 
< 0.1%
1.676901421 × 1012211
 
< 0.1%
Other values (4310) 585381
99.6%
ValueCountFrequency (%)
10341671 137
< 0.1%
11350301 137
< 0.1%
12621993 137
< 0.1%
12685701 137
< 0.1%
17218370 137
< 0.1%
19413601 137
< 0.1%
19413681 137
< 0.1%
20847170 137
< 0.1%
20847200 137
< 0.1%
20847260 137
< 0.1%
ValueCountFrequency (%)
1.906811999 × 1012137
< 0.1%
1.906811999 × 101263
 
< 0.1%
1.906811999 × 1012137
< 0.1%
1.906811998 × 101263
 
< 0.1%
1.906811997 × 101263
 
< 0.1%
1.906811995 × 101263
 
< 0.1%
1.906811995 × 1012137
< 0.1%
1.906811995 × 1012137
< 0.1%
1.906811994 × 1012210
< 0.1%
1.906811993 × 1012211
< 0.1%

Date
Date

Distinct137
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.0 MiB
Minimum2022-09-15 00:00:00
Maximum2023-01-29 00:00:00
2023-03-09T09:24:12.252751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:24:12.432115image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

CampaignName
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.0 MiB
GEN_Type_Date
127271 
GEN_Type_Date _Metis
110618 
GEN_Type_Type
93754 
GEN_Type_Type _Metis
80445 
GEN_Date
72300 
Other values (7)
103166 

Length

Max length30
Median length25
Mean length14.907226
Min length8

Characters and Unicode

Total characters8758800
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGEN_Date
2nd rowGEN_Type_Type
3rd rowGEN_Type_Date
4th rowGEN_Type_Date
5th rowGEN_Date

Common Values

ValueCountFrequency (%)
GEN_Type_Date 127271
21.7%
GEN_Type_Date _Metis 110618
18.8%
GEN_Type_Type 93754
16.0%
GEN_Type_Type _Metis 80445
13.7%
GEN_Date 72300
12.3%
GEN_Date _Metis 62240
10.6%
GEN_From 17682
 
3.0%
GEN_From _Metis 15246
 
2.6%
GEN_Type_Type_From 2960
 
0.5%
GEN_Type_Type_From _Metis 2709
 
0.5%
Other values (2) 2329
 
0.4%

Length

2023-03-09T09:24:12.573190image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
metis 272329
31.7%
gen_type_date 237889
27.7%
gen_type_type 174199
20.3%
gen_date 134540
15.6%
gen_from 32928
 
3.8%
gen_type_type_from 5669
 
0.7%
gen_type_type_type_type 2329
 
0.3%

Most occurring characters

ValueCountFrequency (%)
_ 1290296
14.7%
e 1251699
14.3%
t 644758
 
7.4%
T 606941
 
6.9%
y 606941
 
6.9%
p 606941
 
6.9%
G 587554
 
6.7%
E 587554
 
6.7%
N 587554
 
6.7%
a 372429
 
4.3%
Other values (9) 1616133
18.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4143217
47.3%
Uppercase Letter 3052958
34.9%
Connector Punctuation 1290296
 
14.7%
Space Separator 272329
 
3.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1251699
30.2%
t 644758
15.6%
y 606941
14.6%
p 606941
14.6%
a 372429
 
9.0%
i 272329
 
6.6%
s 272329
 
6.6%
r 38597
 
0.9%
o 38597
 
0.9%
m 38597
 
0.9%
Uppercase Letter
ValueCountFrequency (%)
T 606941
19.9%
G 587554
19.2%
E 587554
19.2%
N 587554
19.2%
D 372429
12.2%
M 272329
8.9%
F 38597
 
1.3%
Connector Punctuation
ValueCountFrequency (%)
_ 1290296
100.0%
Space Separator
ValueCountFrequency (%)
272329
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7196175
82.2%
Common 1562625
 
17.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1251699
17.4%
t 644758
9.0%
T 606941
8.4%
y 606941
8.4%
p 606941
8.4%
G 587554
8.2%
E 587554
8.2%
N 587554
8.2%
a 372429
 
5.2%
D 372429
 
5.2%
Other values (7) 971375
13.5%
Common
ValueCountFrequency (%)
_ 1290296
82.6%
272329
 
17.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8758800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 1290296
14.7%
e 1251699
14.3%
t 644758
 
7.4%
T 606941
 
6.9%
y 606941
 
6.9%
p 606941
 
6.9%
G 587554
 
6.7%
E 587554
 
6.7%
N 587554
 
6.7%
a 372429
 
4.3%
Other values (9) 1616133
18.5%

AdGroupName
Categorical

Distinct1557
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size9.0 MiB
GEN_January_Exact
 
8083
GEN_All Inclusive_Cheap_Exact
 
5754
GEN_February_Exact
 
5617
GEN_Cheap_Family_Exact
 
3973
GEN_Winter_Exact
 
3699
Other values (1552)
560428 

Length

Max length45
Median length39
Mean length22.943246
Min length11

Characters and Unicode

Total characters13480396
Distinct characters56
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGEN_2023_Easter_Exact
2nd rowGEN_5 Star_Beach_BMM
3rd rowGEN_Cheap_Christmas_Exact
4th rowGEN_Deals_January_Exact
5th rowGEN_2023_August_BMM

Common Values

ValueCountFrequency (%)
GEN_January_Exact 8083
 
1.4%
GEN_All Inclusive_Cheap_Exact 5754
 
1.0%
GEN_February_Exact 5617
 
1.0%
GEN_Cheap_Family_Exact 3973
 
0.7%
GEN_Winter_Exact 3699
 
0.6%
GEN_Cheap_Package_Exact 3699
 
0.6%
GEN_March_Exact 3699
 
0.6%
GEN_December_Exact 3425
 
0.6%
GEN_Christmas_Exact 3288
 
0.6%
GEN_All Inclusive_2023_Exact 3077
 
0.5%
Other values (1547) 543240
92.5%

Length

2023-03-09T09:24:12.729956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gen_all 63868
 
8.3%
go 10275
 
1.3%
gen_last 9453
 
1.2%
gen_5 8493
 
1.1%
gen_kids 8357
 
1.1%
gen_january_exact 8083
 
1.0%
minute_exact 7946
 
1.0%
year_exact 6301
 
0.8%
inclusive_cheap_exact 5754
 
0.7%
gen_february_exact 5617
 
0.7%
Other values (1511) 637342
82.6%

Most occurring characters

ValueCountFrequency (%)
_ 1739156
 
12.9%
E 1011695
 
7.5%
a 1009887
 
7.5%
e 794874
 
5.9%
t 679184
 
5.0%
N 616049
 
4.6%
G 612001
 
4.5%
c 599508
 
4.4%
l 439466
 
3.3%
M 434516
 
3.2%
Other values (46) 5544060
41.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6915504
51.3%
Uppercase Letter 3853238
28.6%
Connector Punctuation 1739156
 
12.9%
Decimal Number 782535
 
5.8%
Space Separator 183935
 
1.4%
Other Punctuation 5480
 
< 0.1%
Dash Punctuation 548
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1009887
14.6%
e 794874
11.5%
t 679184
9.8%
c 599508
 
8.7%
l 439466
 
6.4%
r 432886
 
6.3%
x 420179
 
6.1%
s 328394
 
4.7%
u 319985
 
4.6%
i 289991
 
4.2%
Other values (13) 1601150
23.2%
Uppercase Letter
ValueCountFrequency (%)
E 1011695
26.3%
N 616049
16.0%
G 612001
15.9%
M 434516
11.3%
B 265141
 
6.9%
C 172889
 
4.5%
S 108715
 
2.8%
A 106187
 
2.8%
I 78114
 
2.0%
F 74048
 
1.9%
Other values (12) 373883
 
9.7%
Decimal Number
ValueCountFrequency (%)
2 429729
54.9%
0 193168
24.7%
3 109872
 
14.0%
4 40177
 
5.1%
5 8493
 
1.1%
1 548
 
0.1%
8 548
 
0.1%
Connector Punctuation
ValueCountFrequency (%)
_ 1739156
100.0%
Space Separator
ValueCountFrequency (%)
183935
100.0%
Other Punctuation
ValueCountFrequency (%)
& 5480
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 548
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10768742
79.9%
Common 2711654
 
20.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 1011695
 
9.4%
a 1009887
 
9.4%
e 794874
 
7.4%
t 679184
 
6.3%
N 616049
 
5.7%
G 612001
 
5.7%
c 599508
 
5.6%
l 439466
 
4.1%
M 434516
 
4.0%
r 432886
 
4.0%
Other values (35) 4138676
38.4%
Common
ValueCountFrequency (%)
_ 1739156
64.1%
2 429729
 
15.8%
0 193168
 
7.1%
183935
 
6.8%
3 109872
 
4.1%
4 40177
 
1.5%
5 8493
 
0.3%
& 5480
 
0.2%
1 548
 
< 0.1%
8 548
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13480396
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 1739156
 
12.9%
E 1011695
 
7.5%
a 1009887
 
7.5%
e 794874
 
5.9%
t 679184
 
5.0%
N 616049
 
4.6%
G 612001
 
4.5%
c 599508
 
4.4%
l 439466
 
3.3%
M 434516
 
3.2%
Other values (46) 5544060
41.1%

Criteria
Categorical

Distinct4262
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size9.0 MiB
all inclusive 21
 
337
all inclusive 2024
 
274
august holidays 2024
 
274
may holidays 2024
 
274
summer holidays 2024
 
274
Other values (4257)
586121 

Length

Max length47
Median length39
Mean length24.550275
Min length8

Characters and Unicode

Total characters14424612
Distinct characters39
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st roweaster 2023 holidays
2nd row+beach +holidays +5 +star
3rd rowcheap christmas vacations
4th rowjan holiday deals
5th row+august +vacations +2023

Common Values

ValueCountFrequency (%)
all inclusive 21 337
 
0.1%
all inclusive 2024 274
 
< 0.1%
august holidays 2024 274
 
< 0.1%
may holidays 2024 274
 
< 0.1%
summer holidays 2024 274
 
< 0.1%
honeymoon holidays 2024 274
 
< 0.1%
long haul holidays 2024 274
 
< 0.1%
holidays 2024 274
 
< 0.1%
villa holidays 2024 274
 
< 0.1%
family holidays 2024 274
 
< 0.1%
Other values (4252) 584751
99.5%

Length

2023-03-09T09:24:12.920250image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
holidays 321971
 
15.4%
holiday 151677
 
7.3%
2023 108313
 
5.2%
cheap 91908
 
4.4%
inclusive 67293
 
3.2%
all 66408
 
3.2%
in 49919
 
2.4%
2022 44214
 
2.1%
2024 38807
 
1.9%
deals 38577
 
1.9%
Other values (300) 1105193
53.0%

Most occurring characters

ValueCountFrequency (%)
1496726
 
10.4%
a 1346986
 
9.3%
l 997333
 
6.9%
i 995860
 
6.9%
s 909723
 
6.3%
e 891842
 
6.2%
o 840980
 
5.8%
h 724454
 
5.0%
d 664161
 
4.6%
y 634129
 
4.4%
Other values (29) 4922418
34.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11533517
80.0%
Space Separator 1496726
 
10.4%
Decimal Number 787222
 
5.5%
Math Symbol 606462
 
4.2%
Other Punctuation 548
 
< 0.1%
Dash Punctuation 137
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1346986
11.7%
l 997333
 
8.6%
i 995860
 
8.6%
s 909723
 
7.9%
e 891842
 
7.7%
o 840980
 
7.3%
h 724454
 
6.3%
d 664161
 
5.8%
y 634129
 
5.5%
r 532549
 
4.6%
Other values (15) 2995500
26.0%
Decimal Number
ValueCountFrequency (%)
2 430633
54.7%
0 193389
24.6%
3 110642
 
14.1%
4 40725
 
5.2%
5 8767
 
1.1%
1 1696
 
0.2%
8 685
 
0.1%
6 411
 
0.1%
9 137
 
< 0.1%
7 137
 
< 0.1%
Space Separator
ValueCountFrequency (%)
1496726
100.0%
Math Symbol
ValueCountFrequency (%)
+ 606462
100.0%
Other Punctuation
ValueCountFrequency (%)
' 548
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 137
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11533517
80.0%
Common 2891095
 
20.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1346986
11.7%
l 997333
 
8.6%
i 995860
 
8.6%
s 909723
 
7.9%
e 891842
 
7.7%
o 840980
 
7.3%
h 724454
 
6.3%
d 664161
 
5.8%
y 634129
 
5.5%
r 532549
 
4.6%
Other values (15) 2995500
26.0%
Common
ValueCountFrequency (%)
1496726
51.8%
+ 606462
21.0%
2 430633
 
14.9%
0 193389
 
6.7%
3 110642
 
3.8%
4 40725
 
1.4%
5 8767
 
0.3%
1 1696
 
0.1%
8 685
 
< 0.1%
' 548
 
< 0.1%
Other values (4) 822
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14424612
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1496726
 
10.4%
a 1346986
 
9.3%
l 997333
 
6.9%
i 995860
 
6.9%
s 909723
 
6.3%
e 891842
 
6.2%
o 840980
 
5.8%
h 724454
 
5.0%
d 664161
 
4.6%
y 634129
 
4.4%
Other values (29) 4922418
34.1%

CpcBid
Real number (ℝ)

Distinct596
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1496894.5
Minimum84000
Maximum12768000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 MiB
2023-03-09T09:24:13.097449image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum84000
5-th percentile612000
Q1972000
median1356000
Q31800000
95-th percentile2868000
Maximum12768000
Range12684000
Interquartile range (IQR)828000

Descriptive statistics

Standard deviation783306.5
Coefficient of variation (CV)0.52328771
Kurtosis12.465784
Mean1496894.5
Median Absolute Deviation (MAD)408000
Skewness2.3344989
Sum8.7950635 × 1011
Variance6.1356907 × 1011
MonotonicityNot monotonic
2023-03-09T09:24:13.251059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1572000 13492
 
2.3%
1356000 7487
 
1.3%
1092000 6738
 
1.1%
1260000 6631
 
1.1%
1644000 6534
 
1.1%
1104000 6290
 
1.1%
1212000 6273
 
1.1%
1332000 6204
 
1.1%
900000 6143
 
1.0%
1200000 5925
 
1.0%
Other values (586) 515837
87.8%
ValueCountFrequency (%)
84000 1
 
< 0.1%
96000 1
 
< 0.1%
108000 7
 
< 0.1%
120000 18
 
< 0.1%
132000 11
 
< 0.1%
144000 16
 
< 0.1%
156000 38
< 0.1%
168000 39
< 0.1%
180000 58
< 0.1%
192000 52
< 0.1%
ValueCountFrequency (%)
12768000 7
< 0.1%
12024000 2
 
< 0.1%
11496000 3
 
< 0.1%
11448000 2
 
< 0.1%
11316000 2
 
< 0.1%
11124000 7
< 0.1%
10824000 10
< 0.1%
10752000 1
 
< 0.1%
10728000 1
 
< 0.1%
10452000 10
< 0.1%

AbsoluteTopImpressionPercentage
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.30132991
Minimum0
Maximum1.2
Zeros314320
Zeros (%)53.5%
Negative0
Negative (%)0.0%
Memory size6.7 MiB
2023-03-09T09:24:13.416654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.60000002
95-th percentile1.2
Maximum1.2
Range1.2
Interquartile range (IQR)0.60000002

Descriptive statistics

Standard deviation0.39921892
Coefficient of variation (CV)1.3248566
Kurtosis-0.22582053
Mean0.30132991
Median Absolute Deviation (MAD)0
Skewness1.0528615
Sum177047.59
Variance0.15937574
MonotonicityNot monotonic
2023-03-09T09:24:13.587515image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 314320
53.5%
1.200000048 46037
 
7.8%
0.6000000238 29220
 
5.0%
0.3959999979 16659
 
2.8%
0.80400002 12684
 
2.2%
0.3000000119 9912
 
1.7%
0.2399999946 6572
 
1.1%
0.4799999893 6558
 
1.1%
0.8999999762 6408
 
1.1%
0.7200000286 5518
 
0.9%
Other values (91) 133666
22.7%
ValueCountFrequency (%)
0 314320
53.5%
0.0120000001 60
 
< 0.1%
0.02400000021 260
 
< 0.1%
0.03599999845 478
 
0.1%
0.04800000042 657
 
0.1%
0.05999999866 839
 
0.1%
0.0719999969 1136
 
0.2%
0.08399999887 1229
 
0.2%
0.09600000083 1741
 
0.3%
0.1080000028 1461
 
0.2%
ValueCountFrequency (%)
1.200000048 46037
7.8%
1.187999964 18
 
< 0.1%
1.175999999 69
 
< 0.1%
1.164000034 137
 
< 0.1%
1.15199995 201
 
< 0.1%
1.139999986 231
 
< 0.1%
1.128000021 317
 
0.1%
1.116000056 428
 
0.1%
1.103999972 595
 
0.1%
1.092000008 537
 
0.1%

TopImpressionPercentage
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct95
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.66347919
Minimum0
Maximum1.2
Zeros236197
Zeros (%)40.2%
Negative0
Negative (%)0.0%
Memory size6.7 MiB
2023-03-09T09:24:13.756528image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.99599999
Q31.2
95-th percentile1.2
Maximum1.2
Range1.2
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation0.55828428
Coefficient of variation (CV)0.84144958
Kurtosis-1.8322337
Mean0.66347919
Median Absolute Deviation (MAD)0.20400006
Skewness-0.28218454
Sum389829.85
Variance0.31168133
MonotonicityNot monotonic
2023-03-09T09:24:13.923251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 236197
40.2%
1.200000048 212350
36.1%
0.6000000238 8822
 
1.5%
0.80400002 8290
 
1.4%
0.8999999762 7446
 
1.3%
0.9599999785 6682
 
1.1%
0.9959999919 6479
 
1.1%
1.055999994 6428
 
1.1%
1.103999972 6128
 
1.0%
1.128000021 6106
 
1.0%
Other values (85) 82626
 
14.1%
ValueCountFrequency (%)
0 236197
40.2%
0.0719999969 1
 
< 0.1%
0.09600000083 2
 
< 0.1%
0.1080000028 4
 
< 0.1%
0.1199999973 3
 
< 0.1%
0.1319999993 15
 
< 0.1%
0.1439999938 6
 
< 0.1%
0.1560000032 18
 
< 0.1%
0.1679999977 33
 
< 0.1%
0.1800000072 3
 
< 0.1%
ValueCountFrequency (%)
1.200000048 212350
36.1%
1.187999964 2525
 
0.4%
1.175999999 4128
 
0.7%
1.164000034 5064
 
0.9%
1.15199995 5530
 
0.9%
1.139999986 5894
 
1.0%
1.128000021 6106
 
1.0%
1.116000056 5867
 
1.0%
1.103999972 6128
 
1.0%
1.092000008 4863
 
0.8%

SearchImpressionShare
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7826
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.068268
Minimum0
Maximum100
Zeros193541
Zeros (%)32.9%
Negative0
Negative (%)0.0%
Memory size6.7 MiB
2023-03-09T09:24:14.090364image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median42.110001
Q385.709999
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)85.709999

Descriptive statistics

Standard deviation40.331276
Coefficient of variation (CV)0.91519994
Kurtosis-1.6263129
Mean44.068268
Median Absolute Deviation (MAD)42.110001
Skewness0.17690049
Sum25892487
Variance1626.6117
MonotonicityNot monotonic
2023-03-09T09:24:14.249645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 193541
32.9%
100 107201
18.2%
10 44317
 
7.5%
50 23337
 
4.0%
66.66999817 15420
 
2.6%
75 10692
 
1.8%
33.33000183 9093
 
1.5%
80 7590
 
1.3%
60 5705
 
1.0%
83.33000183 5534
 
0.9%
Other values (7816) 165124
28.1%
ValueCountFrequency (%)
0 193541
32.9%
10 44317
 
7.5%
10.02000046 2
 
< 0.1%
10.02999973 1
 
< 0.1%
10.03999996 2
 
< 0.1%
10.05000019 3
 
< 0.1%
10.06000042 3
 
< 0.1%
10.06999969 1
 
< 0.1%
10.07999992 6
 
< 0.1%
10.09000015 6
 
< 0.1%
ValueCountFrequency (%)
100 107201
18.2%
99.69999695 1
 
< 0.1%
99.68000031 1
 
< 0.1%
99.62000275 1
 
< 0.1%
99.58000183 1
 
< 0.1%
99.55000305 1
 
< 0.1%
99.48999786 1
 
< 0.1%
99.48000336 1
 
< 0.1%
99.47000122 1
 
< 0.1%
99.44000244 1
 
< 0.1%

SearchTopImpressionShare
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct92
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.48930752
Minimum0
Maximum1.2
Zeros193576
Zeros (%)32.9%
Negative0
Negative (%)0.0%
Memory size6.7 MiB
2023-03-09T09:24:14.418514image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.396
Q30.93599999
95-th percentile1.2
Maximum1.2
Range1.2
Interquartile range (IQR)0.93599999

Descriptive statistics

Standard deviation0.4670651
Coefficient of variation (CV)0.95454306
Kurtosis-1.5037576
Mean0.48930752
Median Absolute Deviation (MAD)0.396
Skewness0.31756485
Sum287494.59
Variance0.21814981
MonotonicityNot monotonic
2023-03-09T09:24:14.574264image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 193576
32.9%
1.200000048 89221
15.2%
0.1199999973 55158
 
9.4%
0.6000000238 26025
 
4.4%
0.80400002 16415
 
2.8%
0.8999999762 11092
 
1.9%
0.3959999979 11019
 
1.9%
0.9599999785 7971
 
1.4%
0.7200000286 6639
 
1.1%
0.9959999919 6304
 
1.1%
Other values (82) 164134
27.9%
ValueCountFrequency (%)
0 193576
32.9%
0.1199999973 55158
 
9.4%
0.1319999993 1579
 
0.3%
0.1439999938 985
 
0.2%
0.1560000032 1905
 
0.3%
0.1679999977 2159
 
0.4%
0.1800000072 1220
 
0.2%
0.1920000017 932
 
0.2%
0.2039999962 2637
 
0.4%
0.2160000056 1334
 
0.2%
ValueCountFrequency (%)
1.200000048 89221
15.2%
1.187999964 71
 
< 0.1%
1.175999999 440
 
0.1%
1.164000034 831
 
0.1%
1.15199995 1291
 
0.2%
1.139999986 1644
 
0.3%
1.128000021 2118
 
0.4%
1.116000056 2298
 
0.4%
1.103999972 2865
 
0.5%
1.092000008 2545
 
0.4%
Distinct91
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3155065
Minimum0
Maximum1.08
Zeros282797
Zeros (%)48.1%
Negative0
Negative (%)0.0%
Memory size6.7 MiB
2023-03-09T09:24:14.747200image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.096000001
Q30.60000002
95-th percentile1.08
Maximum1.08
Range1.08
Interquartile range (IQR)0.60000002

Descriptive statistics

Standard deviation0.38655034
Coefficient of variation (CV)1.2251739
Kurtosis-0.73706323
Mean0.3155065
Median Absolute Deviation (MAD)0.096000001
Skewness0.86091286
Sum185377.11
Variance0.14942117
MonotonicityNot monotonic
2023-03-09T09:24:14.925803image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 282797
48.1%
1.080000043 55156
 
9.4%
0.6000000238 26026
 
4.4%
0.3959999979 16472
 
2.8%
0.3000000119 11092
 
1.9%
0.80400002 10983
 
1.9%
0.2399999946 7972
 
1.4%
0.4799999893 6639
 
1.1%
0.2039999962 6254
 
1.1%
0.8999999762 5891
 
1.0%
Other values (81) 158272
26.9%
ValueCountFrequency (%)
0 282797
48.1%
0.0120000001 71
 
< 0.1%
0.02400000021 401
 
0.1%
0.03599999845 871
 
0.1%
0.04800000042 1291
 
0.2%
0.05999999866 1647
 
0.3%
0.0719999969 2119
 
0.4%
0.08399999887 2263
 
0.4%
0.09600000083 2900
 
0.5%
0.1080000028 2542
 
0.4%
ValueCountFrequency (%)
1.080000043 55156
9.4%
1.067999959 1578
 
0.3%
1.055999994 1932
 
0.3%
1.04400003 959
 
0.2%
1.031999946 2155
 
0.4%
1.019999981 1223
 
0.2%
1.008000016 935
 
0.2%
0.9959999919 2659
 
0.5%
0.9840000272 1312
 
0.2%
0.9720000029 1156
 
0.2%

Impressions
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1989
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.378937
Minimum0
Maximum12901
Zeros228327
Zeros (%)38.9%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2023-03-09T09:24:15.093703image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q314
95-th percentile87
Maximum12901
Range12901
Interquartile range (IQR)14

Descriptive statistics

Standard deviation136.80074
Coefficient of variation (CV)5.6114317
Kurtosis1559.9833
Mean24.378937
Median Absolute Deviation (MAD)3
Skewness29.862713
Sum14323942
Variance18714.442
MonotonicityNot monotonic
2023-03-09T09:24:15.413439image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 228327
38.9%
2 29574
 
5.0%
1 28488
 
4.8%
3 27978
 
4.8%
4 20605
 
3.5%
5 17198
 
2.9%
6 15819
 
2.7%
7 13322
 
2.3%
8 11573
 
2.0%
9 10565
 
1.8%
Other values (1979) 184105
31.3%
ValueCountFrequency (%)
0 228327
38.9%
1 28488
 
4.8%
2 29574
 
5.0%
3 27978
 
4.8%
4 20605
 
3.5%
5 17198
 
2.9%
6 15819
 
2.7%
7 13322
 
2.3%
8 11573
 
2.0%
9 10565
 
1.8%
ValueCountFrequency (%)
12901 1
< 0.1%
12194 1
< 0.1%
12025 1
< 0.1%
11903 1
< 0.1%
11467 1
< 0.1%
11115 1
< 0.1%
11034 1
< 0.1%
10137 1
< 0.1%
10054 1
< 0.1%
9668 1
< 0.1%

Clicks
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct469
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.589458
Minimum0
Maximum1322.4
Zeros370319
Zeros (%)63.0%
Negative0
Negative (%)0.0%
Memory size6.7 MiB
2023-03-09T09:24:15.583349image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.2
95-th percentile9.6000004
Maximum1322.4
Range1322.4
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation16.07102
Coefficient of variation (CV)6.2063259
Kurtosis1485.7898
Mean2.589458
Median Absolute Deviation (MAD)0
Skewness31.818684
Sum1521446.4
Variance258.27771
MonotonicityNot monotonic
2023-03-09T09:24:15.750899image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 370319
63.0%
1.200000048 79574
 
13.5%
2.400000095 40640
 
6.9%
3.599999905 23907
 
4.1%
4.800000191 15653
 
2.7%
6 10722
 
1.8%
7.199999809 7971
 
1.4%
8.399999619 6072
 
1.0%
9.600000381 4619
 
0.8%
10.80000019 3668
 
0.6%
Other values (459) 24409
 
4.2%
ValueCountFrequency (%)
0 370319
63.0%
1.200000048 79574
 
13.5%
2.400000095 40640
 
6.9%
3.599999905 23907
 
4.1%
4.800000191 15653
 
2.7%
6 10722
 
1.8%
7.199999809 7971
 
1.4%
8.399999619 6072
 
1.0%
9.600000381 4619
 
0.8%
10.80000019 3668
 
0.6%
ValueCountFrequency (%)
1322.400024 1
< 0.1%
1274.400024 1
< 0.1%
1246.800049 1
< 0.1%
1231.199951 1
< 0.1%
1203.599976 1
< 0.1%
1150.800049 1
< 0.1%
1141.199951 1
< 0.1%
1074 1
< 0.1%
1063.199951 1
< 0.1%
1024.800049 1
< 0.1%

Cost
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct6883
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2624619.4
Minimum0
Maximum1.754172 × 109
Zeros370319
Zeros (%)63.0%
Negative0
Negative (%)0.0%
Memory size9.0 MiB
2023-03-09T09:24:15.937213image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31500000
95-th percentile9648000
Maximum1.754172 × 109
Range1.754172 × 109
Interquartile range (IQR)1500000

Descriptive statistics

Standard deviation18296424
Coefficient of variation (CV)6.9710771
Kurtosis1766.9798
Mean2624619.4
Median Absolute Deviation (MAD)0
Skewness34.589184
Sum1.5421056 × 1012
Variance3.3475913 × 1014
MonotonicityNot monotonic
2023-03-09T09:24:16.095218image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 370319
63.0%
792000 849
 
0.1%
744000 849
 
0.1%
960000 847
 
0.1%
816000 838
 
0.1%
828000 808
 
0.1%
888000 807
 
0.1%
696000 805
 
0.1%
936000 799
 
0.1%
720000 797
 
0.1%
Other values (6873) 209836
35.7%
ValueCountFrequency (%)
0 370319
63.0%
12000 22
 
< 0.1%
24000 5
 
< 0.1%
36000 3
 
< 0.1%
60000 22
 
< 0.1%
72000 3
 
< 0.1%
84000 6
 
< 0.1%
96000 9
 
< 0.1%
108000 24
 
< 0.1%
120000 27
 
< 0.1%
ValueCountFrequency (%)
1754172000 1
< 0.1%
1490676000 1
< 0.1%
1472844000 1
< 0.1%
1414968000 1
< 0.1%
1396680000 1
< 0.1%
1393980000 1
< 0.1%
1388676000 1
< 0.1%
1365960000 1
< 0.1%
1341528000 1
< 0.1%
1316916000 1
< 0.1%

Sessions
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct470
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5893171
Minimum0
Maximum1322.4
Zeros370324
Zeros (%)63.0%
Negative0
Negative (%)0.0%
Memory size6.7 MiB
2023-03-09T09:24:16.269582image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.2
95-th percentile9.6000004
Maximum1322.4
Range1322.4
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation16.07053
Coefficient of variation (CV)6.2064743
Kurtosis1485.8749
Mean2.5893171
Median Absolute Deviation (MAD)0
Skewness31.819334
Sum1521363.6
Variance258.2619
MonotonicityNot monotonic
2023-03-09T09:24:16.433230image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 370324
63.0%
1.200000048 79571
 
13.5%
2.400000095 40643
 
6.9%
3.599999905 23909
 
4.1%
4.800000191 15648
 
2.7%
6 10725
 
1.8%
7.199999809 7966
 
1.4%
8.399999619 6074
 
1.0%
9.600000381 4620
 
0.8%
10.80000019 3666
 
0.6%
Other values (460) 24408
 
4.2%
ValueCountFrequency (%)
0 370324
63.0%
1.200000048 79571
 
13.5%
2.400000095 40643
 
6.9%
3.599999905 23909
 
4.1%
4.800000191 15648
 
2.7%
6 10725
 
1.8%
7.199999809 7966
 
1.4%
8.399999619 6074
 
1.0%
9.600000381 4620
 
0.8%
10.80000019 3666
 
0.6%
ValueCountFrequency (%)
1322.400024 1
< 0.1%
1274.400024 1
< 0.1%
1246.800049 1
< 0.1%
1231.199951 1
< 0.1%
1203.599976 1
< 0.1%
1150.800049 1
< 0.1%
1141.199951 1
< 0.1%
1074 1
< 0.1%
1063.199951 1
< 0.1%
1024.800049 1
< 0.1%

Cost_gbp
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct6880
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6769503
Minimum0
Maximum1789.2555
Zeros370324
Zeros (%)63.0%
Negative0
Negative (%)0.0%
Memory size6.7 MiB
2023-03-09T09:24:16.594964image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.53
95-th percentile9.8409595
Maximum1789.2555
Range1789.2555
Interquartile range (IQR)1.53

Descriptive statistics

Standard deviation18.661524
Coefficient of variation (CV)6.971188
Kurtosis1767.1128
Mean2.6769503
Median Absolute Deviation (MAD)0
Skewness34.589706
Sum1572852.9
Variance348.25247
MonotonicityNot monotonic
2023-03-09T09:24:16.761420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 370324
63.0%
0.7588800192 849
 
0.1%
0.8078399897 849
 
0.1%
0.9792000055 847
 
0.1%
0.8323199749 838
 
0.1%
0.9057599902 807
 
0.1%
0.8445600271 807
 
0.1%
0.7099199891 804
 
0.1%
0.9547200203 799
 
0.1%
0.7343999743 797
 
0.1%
Other values (6870) 209833
35.7%
ValueCountFrequency (%)
0 370324
63.0%
0.01224000007 22
 
< 0.1%
0.02448000014 5
 
< 0.1%
0.03672000021 3
 
< 0.1%
0.06120000035 22
 
< 0.1%
0.07344000041 3
 
< 0.1%
0.08568000048 6
 
< 0.1%
0.09792000055 9
 
< 0.1%
0.1101600006 24
 
< 0.1%
0.1224000007 27
 
< 0.1%
ValueCountFrequency (%)
1789.255493 1
< 0.1%
1520.489502 1
< 0.1%
1502.300903 1
< 0.1%
1443.267334 1
< 0.1%
1424.613647 1
< 0.1%
1421.859619 1
< 0.1%
1416.449463 1
< 0.1%
1393.279175 1
< 0.1%
1368.358521 1
< 0.1%
1343.254272 1
< 0.1%

Margin
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct18624
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9361715
Minimum-122.14581
Maximum2349.5994
Zeros568930
Zeros (%)96.8%
Negative21
Negative (%)< 0.1%
Memory size6.7 MiB
2023-03-09T09:24:16.933928image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-122.14581
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2349.5994
Range2471.7452
Interquartile range (IQR)0

Descriptive statistics

Standard deviation24.626743
Coefficient of variation (CV)12.719299
Kurtosis1638.5544
Mean1.9361715
Median Absolute Deviation (MAD)0
Skewness32.5578
Sum1137605.3
Variance606.47644
MonotonicityNot monotonic
2023-03-09T09:24:17.099102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 568930
96.8%
5.330278873 2
 
< 0.1%
67.53459167 1
 
< 0.1%
20.54398155 1
 
< 0.1%
74.61479187 1
 
< 0.1%
41.43907928 1
 
< 0.1%
12.29122353 1
 
< 0.1%
71.10586548 1
 
< 0.1%
32.89949036 1
 
< 0.1%
4.488942146 1
 
< 0.1%
Other values (18614) 18614
 
3.2%
ValueCountFrequency (%)
-122.1458054 1
< 0.1%
-83.62361908 1
< 0.1%
-83.52625275 1
< 0.1%
-83.43190765 1
< 0.1%
-32.88007736 1
< 0.1%
-29.41637802 1
< 0.1%
-19.80197906 1
< 0.1%
-16.08289146 1
< 0.1%
-9.859479904 1
< 0.1%
-6.271931171 1
< 0.1%
ValueCountFrequency (%)
2349.599365 1
< 0.1%
2171.451904 1
< 0.1%
2006.36377 1
< 0.1%
1935.915283 1
< 0.1%
1902.381958 1
< 0.1%
1896.382446 1
< 0.1%
1850.828247 1
< 0.1%
1797.886108 1
< 0.1%
1783.546875 1
< 0.1%
1771.375366 1
< 0.1%

ROI_gbp
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct15964
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35440124
Minimum-57.280102
Maximum1582.5066
Zeros571589
Zeros (%)97.3%
Negative21
Negative (%)< 0.1%
Memory size6.7 MiB
2023-03-09T09:24:17.264697image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-57.280102
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1582.5066
Range1639.7867
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.0511198
Coefficient of variation (CV)19.895867
Kurtosis8380.4639
Mean0.35440124
Median Absolute Deviation (MAD)0
Skewness68.26133
Sum208229.86
Variance49.718292
MonotonicityNot monotonic
2023-03-09T09:24:17.431029image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 571589
97.3%
4.954443455 2
 
< 0.1%
1.238139987 2
 
< 0.1%
1.482692361 1
 
< 0.1%
0.3272510767 1
 
< 0.1%
6.681012154 1
 
< 0.1%
0.4683260024 1
 
< 0.1%
62.15295792 1
 
< 0.1%
0.4003099203 1
 
< 0.1%
1.247957706 1
 
< 0.1%
Other values (15954) 15954
 
2.7%
ValueCountFrequency (%)
-57.28010178 1
< 0.1%
-37.49472809 1
< 0.1%
-28.348526 1
< 0.1%
-20.98656845 1
< 0.1%
-5.641546726 1
< 0.1%
-4.656144619 1
< 0.1%
-4.570081234 1
< 0.1%
-1.459805846 1
< 0.1%
-1.203261614 1
< 0.1%
-1.094004273 1
< 0.1%
ValueCountFrequency (%)
1582.506592 1
< 0.1%
1121.160278 1
< 0.1%
799.2728271 1
< 0.1%
795.7611084 1
< 0.1%
756.4002075 1
< 0.1%
728.3591309 1
< 0.1%
696.4936523 1
< 0.1%
696.1707153 1
< 0.1%
633.7678833 1
< 0.1%
587.43573 1
< 0.1%

Interactions

2023-03-09T09:24:03.245341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:22:59.812329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:03.212922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:06.812680image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:12.018368image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:15.873823image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:19.431218image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:23.883192image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:28.913307image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:33.431443image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:37.320357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:41.057239image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:46.323644image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:50.993028image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:55.668428image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:59.458382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:24:03.468841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:00.047469image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:03.429181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:07.077837image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:12.270595image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:16.116160image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:19.699271image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:24.203946image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:29.213613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:33.654405image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:37.534850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:41.274484image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:46.582959image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:51.247402image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:55.911790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:59.858638image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:24:03.736927image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:00.263544image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:03.650805image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:07.316302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:12.603440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:16.340946image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:19.971354image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:24.676686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:29.469218image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:33.898509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:37.758828image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:41.509989image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:46.929545image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:51.539396image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:56.152040image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:24:00.141672image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:24:03.980615image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:00.470288image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:03.882978image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:07.557463image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:12.838363image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:16.565182image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:20.217090image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:25.026521image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:29.700343image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:34.143210image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:37.991003image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:41.882867image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:47.438299image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:51.799343image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:56.409465image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:24:00.362051image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:24:04.217119image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:00.661979image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:04.106150image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:07.799840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:13.060470image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:16.773489image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:20.459654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:25.374013image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:29.930383image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:34.524718image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:38.204273image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:42.261607image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:47.754089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:52.034008image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:56.629449image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:24:00.592836image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:24:04.496250image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:00.870018image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:04.325841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:08.203194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:13.291591image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:16.984387image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:20.700674image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:25.789976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:30.177075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:34.769891image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:38.422495image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:42.549926image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:48.131028image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:52.275406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:56.861148image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:24:00.807309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:24:04.778060image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:01.079018image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:04.549079image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:08.612041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:13.529023image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:17.198668image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:21.079742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:26.162637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:30.426463image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:35.004131image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:38.667226image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:43.003196image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:48.547539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:52.499454image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:57.090469image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:24:01.036810image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:24:05.069758image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:01.287504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:04.757507image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:08.942857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:13.757091image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:17.414117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:21.345986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:26.654223image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:30.655719image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:35.234566image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:38.875593image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:43.309879image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:48.801208image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:52.801606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:57.351886image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:24:01.258117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:24:05.379631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:01.491857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:04.972481image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:09.295325image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:13.975744image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:17.642029image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:21.746703image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:27.004749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:30.912655image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:35.461484image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:39.137761image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:43.649940image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:49.067416image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:53.072746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:57.583590image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:24:01.480037image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:24:05.735246image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:01.701273image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:05.197585image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:09.943242image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:14.192602image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:17.857747image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:22.101074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:27.311535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:31.288174image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:35.682690image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:39.392427image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:44.054334image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:49.309575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:53.394059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:57.809891image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:24:01.698238image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:24:06.096422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:01.912977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:05.423033image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:10.287859image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:14.428338image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:18.075764image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:22.363556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:27.550699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:31.612781image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:35.911939image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:39.625548image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:44.421481image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:49.556997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:53.746962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:58.032131image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:24:01.916360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:24:06.346668image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:02.124283image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:05.648036image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:10.602099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:14.645064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:18.291109image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:22.646287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:27.784772image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:31.923664image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:36.134745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:39.857113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:44.730881image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:49.790308image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:54.060530image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:58.264129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:24:02.161494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:24:06.628524image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:02.338037image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:05.880827image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:10.878851image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:14.881114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:18.513068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:22.963247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:28.025593image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:32.273706image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:36.362827image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:40.115156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:45.087133image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:50.029203image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:54.452561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:58.515085image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:24:02.389968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:24:06.864656image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:02.555243image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:06.097138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:11.116795image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:15.131140image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:18.727896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:23.191499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:28.259817image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:32.633301image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:36.592325image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:40.350123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:45.329211image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:50.292636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:54.843627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:58.778335image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:24:02.604221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:24:07.109831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:02.788416image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:06.330539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:11.351144image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:15.388319image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:18.965954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:23.420189image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:28.475186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:32.966441image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:36.826186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:40.586238image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:45.727852image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:50.530010image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:55.195304image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:59.007099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:24:02.840298image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:24:07.331831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:02.990583image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:06.541695image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:11.663483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:15.617725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:19.193500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:23.627466image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:28.677059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:33.198797image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:37.093590image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:40.824477image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:46.060552image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:50.761169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:55.456608image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:23:59.219069image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-09T09:24:03.042948image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-03-09T09:24:17.599877image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
AdGroupIdCampaignIdCriterionIdCpcBidAbsoluteTopImpressionPercentageTopImpressionPercentageSearchImpressionShareSearchTopImpressionShareSearchRankLostTopImpressionShareImpressionsClicksCostSessionsCost_gbpMarginROI_gbpCampaignName
AdGroupId1.000-0.695-0.1110.0380.0770.0610.0190.0290.1180.1110.0680.0600.0680.0600.0170.0190.818
CampaignId-0.6951.0000.015-0.113-0.020-0.036-0.009-0.014-0.107-0.083-0.033-0.034-0.033-0.034-0.025-0.0241.000
CriterionId-0.1110.0151.0000.012-0.159-0.175-0.146-0.161-0.209-0.185-0.146-0.142-0.146-0.142-0.048-0.0460.222
CpcBid0.038-0.1130.0121.0000.0960.0430.0380.051-0.120-0.039-0.0420.006-0.0420.006-0.013-0.0150.059
AbsoluteTopImpressionPercentage0.077-0.020-0.1590.0961.0000.6990.6500.6880.2050.7120.5970.6140.5970.6140.1230.1330.056
TopImpressionPercentage0.061-0.036-0.1750.0430.6991.0000.8110.8740.2130.6740.4710.4820.4710.4820.0860.0860.050
SearchImpressionShare0.019-0.009-0.1460.0380.6500.8111.0000.9770.1800.7130.5130.5250.5130.5250.1120.1150.058
SearchTopImpressionShare0.029-0.014-0.1610.0510.6880.8740.9771.0000.1490.7120.5330.5470.5330.5470.1170.1210.055
SearchRankLostTopImpressionShare0.118-0.107-0.209-0.1200.2050.2130.1800.1491.0000.4610.2680.2480.2680.2480.0730.0670.054
Impressions0.111-0.083-0.185-0.0390.7120.6740.7130.7120.4611.0000.7950.7850.7950.7850.2160.2300.012
Clicks0.068-0.033-0.146-0.0420.5970.4710.5130.5330.2680.7951.0000.9901.0000.9900.2300.2670.007
Cost0.060-0.034-0.1420.0060.6140.4820.5250.5470.2480.7850.9901.0000.9901.0000.2250.2610.007
Sessions0.068-0.033-0.146-0.0420.5970.4710.5130.5330.2680.7951.0000.9901.0000.9900.2300.2670.007
Cost_gbp0.060-0.034-0.1420.0060.6140.4820.5250.5470.2480.7850.9901.0000.9901.0000.2250.2610.007
Margin0.017-0.025-0.048-0.0130.1230.0860.1120.1170.0730.2160.2300.2250.2300.2251.0000.9250.005
ROI_gbp0.019-0.024-0.046-0.0150.1330.0860.1150.1210.0670.2300.2670.2610.2670.2610.9251.0000.004
CampaignName0.8181.0000.2220.0590.0560.0500.0580.0550.0540.0120.0070.0070.0070.0070.0050.0041.000

Missing values

2023-03-09T09:24:07.762336image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-09T09:24:08.797987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AdGroupIdCampaignIdCriterionIdDateCampaignNameAdGroupNameCriteriaCpcBidAbsoluteTopImpressionPercentageTopImpressionPercentageSearchImpressionShareSearchTopImpressionShareSearchRankLostTopImpressionShareImpressionsClicksCostSessionsCost_gbpMarginROI_gbp
01463722408401902601654512243588597352022-10-07GEN_DateGEN_2023_Easter_Exacteaster 2023 holidays1824000.00.0000.00010.0000000.1201.08000.00.00.00.000000.00.0
1143665838613190326099794364686143762022-11-17GEN_Type_TypeGEN_5 Star_Beach_BMM+beach +holidays +5 +star2160000.00.3481.20053.8499980.6480.55290.00.00.00.000000.00.0
2142284478245190326150521515563162022-09-21GEN_Type_DateGEN_Cheap_Christmas_Exactcheap christmas vacations708000.00.9001.200100.0000001.2000.00091.21008000.01.21.028160.00.0
314228448044519032615052116906337632022-10-20GEN_Type_DateGEN_Deals_January_Exactjan holiday deals2544000.00.0000.0000.0000000.0000.00000.00.00.00.000000.00.0
41463722412801902601654514161999612322022-11-17GEN_DateGEN_2023_August_BMM+august +vacations +20231260000.00.0000.000100.0000000.1201.08030.00.00.00.000000.00.0
514366582853319032609979167363495322022-11-16GEN_Type_TypeGEN_Cheap_Low Deposit_Exactcheap holiday deposits2100000.00.0000.60050.0000000.3000.90040.00.00.00.000000.00.0
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587586142284510685190326150523421813871392022-12-01GEN_Type_Date _MetisGEN_Adult_Winter_BMM+adult +only +winter +sun +holidays1008000.01.201.2100.0000001.2000.00030.00.00.00.000000.00.0
5875871463722444401902601654517431966806842022-11-04GEN_DateGEN_2024_December_Exactholidays in december 2024492000.00.000.00.0000000.0000.00000.00.00.00.000000.00.0
5875881422844852051903261505254018248702022-12-10GEN_Type_Date _MetisGEN_Singles_Christmas_Exactxmas singles holidays1356000.00.721.286.9599991.0440.156338.47284000.08.47.429680.00.0
58758914366583205319032609979113910290122023-01-01GEN_Type_Type _MetisGEN_Group_Luxury_Exactluxury group holiday2136000.00.000.00.0000000.0000.00000.00.00.00.000000.00.0
5875901463722278801902601654526438434412022-10-29GEN_DateGEN_December_Exactholiday december912000.00.001.287.5000001.0560.156101.2672000.01.20.685440.00.0
587591143869938237190326155413102538033782022-11-06GEN_FromGEN_Bristol_BMM+holidays +from +bristol1392000.01.201.225.0000000.3000.90021.22208000.01.22.252160.00.0
58759214228447700519032615052429976653782022-12-28GEN_Type_Date _MetisGEN_Beach_Christmas_Exactxmas beach holiday1392000.00.000.00.0000000.0000.00000.00.00.00.000000.00.0